ALI multilayered co-cultures mimic biochemical mechanisms of the cancer cell-fibroblast cross-talk involved in NSCLC MultiDrug Resistance
Bibliographic record
Abstract
BACKGROUND: Lung cancer is the leading cause of cancer-related deaths worldwide. This study focuses on its most common form, Non-Small-Cell Lung Cancer (NSCLC). No cure exists for advanced NSCLC, and patient prognosis is extremely poor. Efforts are currently being made to develop effective inhaled NSCLC therapies. However, at present, reliable preclinical models to support the development of inhaled anti-cancer drugs do not exist. This is due to the oversimplified nature of currently available in vitro models, and the significant interspecies differences between animals and humans. METHODS: We have recently established 3D Multilayered Cell Cultures (MCCs) of human NSCLC (A549) cells grown at the Air-Liquid Interface (ALI) as the first in vitro tool for screening the efficacy of inhaled anti-cancer drugs. Here, we present an improved in vitro model formed by growing A549 cells and human fibroblasts (MRC-5 cell line) as an ALI multilayered co-culture. The model was characterized over 14-day growth and tested for its response to four benchmarking chemotherapeutics. RESULTS: ALI multilayered co-cultures showed an increased resistance to the four drugs tested as compared to ALI multilayered mono-cultures. The signalling pathways involved in the culture MultiDrug Resistance (MDR) were influenced by the cancer cell-fibroblast cross-talk, which was mediated through TGF-β1 release and subsequent activation of the PI3K/AKT/mTOR pathway. As per in vivo conditions, when inhibiting mTOR phosphorylation, MDR was triggered by activation of the MEK/ERK pathway activation and up-regulation in cIAP-1/2 expression. CONCLUSIONS: Our study opens new research avenues for the development of alternatives to animal-based inhalation studies, impacting the development of anti-NSCLC drugs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".